Abstract
There is currently only limited understanding of the genetic aetiology of obstructive sleep apnoea (OSA). The aim of our study is to identify genetic loci associated with OSA risk and to test if OSA and its comorbidities share a common genetic background.
We conducted the first large-scale genome-wide association study of OSA using FinnGen Study (217,955 individuals) with 16,761 OSA patients identified using nationwide health registries.
We estimated 8.3% [0.06-0.11] heritability and identified five loci associated with OSA (P < 5.0 × 10−8): rs4837016 near GTPase activating protein and VPS9 domains 1 (GAPVD1), rs10928560 near C-X-C motif chemokine receptor 4 (CXCR4), rs185932673 near Calcium/calmodulin-dependent protein kinase ID (CAMK1D) and rs9937053 near Fat mass and obesity-associated protein (FTO) - a variant previously associated with body mass index (BMI). In a BMI-adjusted analysis, an association was observed for rs10507084 near Rhabdomyosarcoma 2 associated transcript (RMST)/NEDD1 gamma-tubulin ring complex targeting factor (NEDD1).
We found genetic correlations between OSA and BMI (rg=0.72 [0.62-0.83]) and with comorbidities including hypertension, type 2 diabetes (T2D), coronary heart disease (CHD), stroke, depression, hypothyroidism, asthma and inflammatory rheumatic diseases (IRD) (rg > 0.30). Polygenic risk score (PRS) for BMI showed 1.98-fold increased OSA risk between the highest and the lowest quintile and Mendelian randomization supported a causal relationship between BMI and OSA.
Our findings support the causal link between obesity and OSA and joint genetic basis between OSA and comorbidities.
Introduction
Obstructive sleep apnoea (OSA) is a severe sleep disorder affecting at least 9% of the population. Prevalence increases with higher age reaching over 35% in individuals over 60 years of age1. Despite a recognized health impact and available diagnostic tools and treatments the condition remains underdiagnosed2, 3. OSA is characterized by repetitive episodes of nocturnal breathing cessation due to upper airway collapse resulting in mild to severe sleep deprivation and dysregulation of sleep, breathing and blood pressure. These conditions may lead to serious comorbidities through intermittent hypoxia, systemic inflammation and sympathetic activation4. Furthermore, OSA is influenced by multiple risk factors such as obesity, male sex, family history of OSA, high age and problems of upper airway flow or jaw anatomy5.
Consequently, OSA is a serious public health problem due to its many cardiometabolic comorbidities including an increased risk to coronary heart disease (CHD), type 2 diabetes (T2D) and its complications6 and ultimately, increased mortality7. In addition, comorbidities such as depression8, hypothyroidism9, asthma10 and inflammatory rheumatic diseases (IRD)11 are linked with OSA. IRD might manifest as a comorbidity of OSA through the affection of the temporomandibular joint, which rotates the lower jaw backward causing narrowing of the upper airway12.
Genetic studies provide a tool to identify independent genetic risk factors that modulate disease risk, and to examine causal pathways between comorbidity traits. Genome-wide association studies (GWAS) in OSA patients have previously identified associations with OSA severity measured with apnoea-hypopnea index (AHI, number of apnoeas and hypopneas per hour of sleep) or respiratory event duration13–15. The genome-wide significant findings from these studies and the corresponding associations our study are found in Supplementary Table 1. Larger-scale GWAS studies have been performed on OSA-related phenotypes such as snoring16. However, knowledge about OSA predisposing genetic loci is thus far limited17.
To test genetic associations with OSA we utilised FinnGen study with genetic profiling for 217,955 individuals and OSA diagnosis based on International Statistical Classification of Diseases (ICD) codes obtained from the Finnish National Hospital Discharge Registry and the Causes of Death Registry. The registries have excellent validity and coverage18. Combining the OSA diagnosis (ICD-10: G47.3, ICD-9: 3472) and related risk factors and comorbidities with the genotyping data allows identification of risk variants, helps elucidating biological disease mechanisms and enables evaluation of OSA-related disease burden on a population level.
The aim of the study is to identify genetic loci associated with OSA risk and to test if OSA and its comorbidities share a common genetic background. To our knowledge, this is the first population-level longitudinal GWAS study regarding OSA.
Materials and Methods
General information
First, using the FinnGen data, a GWAS was calculated for 2,925 ICD-code based phenotype definitions including OSA. Second, we selected into further analyses comorbidities which have previously been shown to associate with OSA in epidemiological studies, including obesity19, hypertension20, T2D21, CHD, stroke22, depression8, hypothyroidism9, asthma10 and IRD11, 12.
Study sample in FinnGen
FinnGen (https://www.finngen.fi/en) is a large biobank study that aims to genotype 500,000 Finns and combine this data with longitudinal registry data including The National Hospital Discharge Registry, Causes of Death Registry and medication reimbursement registries, all these using unique national personal identification codes. FinnGen includes prospective and retrospective epidemiological and disease-based cohorts as well as hospital biobank samples. The data consists of 218,792 individuals until the spring of 2020. FinnGen’s genotyping and imputation protocol is described in Supplementary Information.
To examine OSA patients more specifically 837 individuals who had ICD-code G47 (Sleep disorders) were excluded from the controls and thus the remaining sample size was 217,955 participants. Of them, 16,761 (7.7%) had OSA diagnosis and 10,557 (63.0%) of OSA patients were male. Baseline characteristics and OSA comorbidities of the participants are presented in Table 1. Differences in baseline demographics and clinical characteristics were tested using logistic regression model. The model was adjusted for sex, age and 10 first principal components (PC), except the model for age was adjusted for sex and 10 first PCs and the model for sex was adjusted for age and 10 first PCs.
The diagnosis of OSA was based on ICD-codes (ICD-10: G47.3, ICD-9: 3472A), which were obtained from the Finnish National Hospital Discharge Registry and the Causes of Death Registry. This diagnosis is based on subjective symptoms, clinical examination and sleep registration applying AHI≥5/hour or respiratory disturbance index (RDI)≥5/hour. By combining ICD-codes from different registries, we generated disease endpoints. Supplementary Table 2 describes how endpoints were constructed for each phenotype.
All prescription medicine purchases were retrieved from the Social Insurance Institution of Finland (KELA) registry for prescription drug purchases, since 1995 (excluding over-the-counter medicines and medication administered at hospitals). The drugs are coded by the Anatomical Therapeutic Chemical (ATC) Classification System.
Study samples in other cohorts
UK Biobank (UKBB, https://www.ukbiobank.ac.uk/) is a major national and international health resource, with the aim of improving the prevention, diagnosis and treatment of a wide range of serious and life-threatening illnesses. UKBB recruited 500,000 people in 2006-2010 from across the United Kingdom. OSA diagnosis was based on ICD-10: G47.3. The study sample in the UKBB included 4,471 OSA cases and 403,723 controls.
The Estonian Biobank is a population-based biobank of the Estonian Genome Center at the University of Tartu (EGCUT, www.biobank.ee). The cohort size is currently close to 150,000 participants. Patients were selected by ICD-10: G47.3. For additional conformation of the diagnosis treatment service codes from the Health Insurance Fund were also used. The study sample in the EGCUT included 4,930 OSA patients and 61,056 controls.
The All New Diabetics in Scania (ANDIS, http://andis.ludc.med.lu.se/) aims to recruit all incident cases of diabetes within Scania County in Southern Sweden. All health care providers in the region were invited; the current registration covered 14,625 patients. OSA was defined by ICD-10: G47.3. The study sample included 947 OSA patients and 9,829 controls.
GWAS
A total of 218,792 samples from FinnGen Data Freeze 5 with 2,925 disease endpoints were analyzed using Scalable and Accurate Implementation of Generalized mixed model (SAIGE), which uses saddle point approximation (SPA) to calibrate unbalanced case-control ratios23. Analyses were adjusted for age, sex, genotyping chip, genetic relationship and first 10 PCs. For OSA, we performed GWAS in a similar manner (n=217,955, including 16,761 OSA patients and 201,194 controls), but adjusting also for body mass index (BMI) (n=159,731, including 12,759 OSA patients and 146,972 controls).
For the replication of the FinnGen OSA GWAS results we merged the evidence from the UKBB, EGCUT and ANDIS cohorts. The results were combined using inverse-variance weighted fixed-effect meta-analysis. The merged data consisted 10,348 OSA cases and 474,608 controls.
The GWAS using UKBB data was calculated using SAIGE23. This subset included 4,471 OSA cases and 403,723 controls and was adjusted for birth year, sex, genetic relatedness and the first 4 PCs. In the EGCUT the data were analyzed using SAIGE and the model was adjusted for age, sex, genetic relatedness and the first 10 PCs and included 4,930 OSA patients and 61,056 controls. In ANDIS, the GWAS was calculated using logistic regression model, which was adjusted for age, sex and first 10 PCs. The analysis included 947 cases and 9,829 controls.
Linkage disequilibrium score regression (LDSC)
To estimate single nucleotide polymorphism (SNP) -based heritability, genetic correlation and tissue specific SNP-heritability we used LDSC-software24. LDSC uses linkage disequilibrium (LD) score regression method, which quantifies the contribution of each variant by examining the relationship between test statistics and LD. In calculation we used LD scores calculated from the 1000 Genomes Project25. To restrict to a set of common, well-imputed variants, we retained only those SNPs in the HapMap 3 reference panel26.
To study genetic correlations between OSA, BMI, hypertension, T2D, CHD, stroke, depression, hypothyroidism, asthma and IRD we used summary statistics from the FinnGen data. For sleep traits we used summary statistics derived from the UKBB data. Study subjects self-reported sleep duration, sleepiness27 and chronotype28. Sleep efficiency (sleep duration divided by the time between the start and end of the first and last nocturnal inactivity period, respectively) was based on accelerometer-derived measures29. For tissue specific SNP-heritability we used a method, which combined data from Encyclopedia of DNA Elements (ENCODE, https://www.encodeproject.org/) and the Genotype-Tissue Expression (GTEx, https://gtexportal.org/home/) resources30, 31.
Polygenic risk score (PRS) and Mendelian randomization (MR)
PRS for BMI was calculated using summary statistics for 996,250 variants32. The posterior effect sizes were calculated with PRS-CS utilising method33 and the score was calculated using Plink2 (https://www.cog-genomics.org/plink/2.0/) for the FinnGen data.
We performed MR analysis to investigate the causality between BMI and OSA using independent BMI SNPs32. A genetic variant associated with the exposure of interest (genetic instrument) is used to test the causal relationship with the exposure (BMI) and outcome (OSA)34.
Gene based analysis
Gene-based tests were performed using Multi-marker Analysis of GenoMic Annotation (MAGMA) as implemented on the Functional Mapping and Annotation (FUMA) platform, which provides aggregate association p-values based on all variants located within a gene and its regulatory region using information from 18 biological data repositories and tools35. This analysis includes a gene-based test to detect significant SNPs associated with OSA using FinnGen OSA summary statistics.
Results
OSA correlates strongly with cardiovascular and metabolic traits
To estimate strengths of associations between OSA and comorbidities we utilised data from 217,955 individuals who have participated in the FinnGen project. 16,761 (7.7%) had OSA diagnosis and 10,557 (63%) of cases were male. The diagnoses were derived from ICD-codes in the Finnish National Hospital Discharge Registry and from the Causes of Death Registry. Baseline characteristics of the FinnGen participants and odds for OSA associated comorbidities are presented in Table 1.
GWAS of OSA reveals BMI dependent and independent associations
We estimated the heritability for OSA in FinnGen to be 8.3% [0.06-0.11] before and 6.0% [0.04-0.08] after BMI adjustment. In a genome-wide association test, five distinct genetic loci were associated with OSA (P < 5.0 × 10−8), outlined in Table 2 and Figure 1a and regional associations in Supplementary Figure 1. The lead variant in a locus on chromosome 16 was rs9937053, an intronic variant near Fat mass and obesity-associated protein (FTO), P = 4.3 × 10−16. In chromosome 12, the lead variant was rs10507084, near Rhabdomyosarcoma 2 associated transcript (RMST)/NEDD1 gamma-tubulin ring complex targeting factor (NEDD1), P = 2.8 × 10−11, where RMST, a long non-coding RNA, was the nearest gene and NEDD1 the nearest protein coding gene. On chromosome 10, the lead variant was rs185932673, an intronic variant near Calcium/calmodulin-dependent protein kinase ID (CAMK1D), P = 2.4 × 10−8. In chromosome 9, the lead variant was rs4837016 near GTPase activating protein and VPS9 Domains 1 (GAPVD1), P = 1.5 × 10−8 and in chromosome 2, the lead variant rs10928560 was near C-X-C motif chemokine receptor 4 (CXCR4), P = 2.8 x 10−8. Four out of five of these OSA associated lead variants have also been previously associated with BMI (p<0.01)36–38, with the exception of rs10507084 at the RMST/NEDD1 locus. Conditional analyses of the associated loci did not suggest any additional associations. Adjusting for BMI did not affect the association for variant rs10507084 (Figure 1b and Table 2), (ORunadjusted = 1.11[1.08-1.15], P=2.8 × 10−11 vs. ORBMI adjusted = 1.12[1.08-1.17], P=9.7 × 10−10) suggesting BMI-independent mechanisms for rs10507084 in OSA predisposition.
As an exploratory analysis we used MAGMA. This tool annotates FinnGen OSA summary statistics based on 18 biological data repositories and tools35. Using MAGMA, we detected 25 significant associations (P < 2.54 × 10−6) with various biological processes, which were driven by the same loci as the significant GWAS variants in FTO and GAPVD1 (Supplementary Figure 2a). Similarly, the gene-based test for BMI-adjusted OSA revealed three further associated genes (Supplementary Figure 2b).
We performed a phenome-wide association analysis (PheWAS) using the FinnGen data and examined the associations between the lead SNPs and 2,925 disease endpoints. Rs10507084 was specific for OSA also after BMI adjustment suggesting an independent role from cardiometabolic traits for the association between rs10507084 and OSA (Figure 2a). In addition, there was a strong correlation between rs10507084 and the use of antidepressants (OR=1.013[1.007-1.019], P=4.4 × 10−6) (Figure 2b).
Genetic correlations and MR connect OSA with cardiovascular outcomes and dysregulation of metabolism
To study the potential common genetic background of OSA and its known epidemiological correlates, we computed genetic correlations between OSA and its comorbidities using FinnGen summary statistics. The results showed strong genetic correlations between OSA and BMI (rg = 0.72, [0.62-0.83], P=3.49 × 10−40) and between OSA and comorbidities: hypertension (rg=0.35, [0.23-0.48], P=4.06 × 10−8), T2D (rg=0.52, [0.37-0.66], P=6.40 × 10−12), CHD (rg=0.38, [0.17-0.58], P=3.84 × 10−4), stroke (rg=0.33, [0.03-0.63], P=2.93 × 10−2), depression (rg=0.43, [0.27-0.60], P=2.79 × 10−7), hypothyroidism (rg=0.40, [0.27-0.54], P=7.07 × 10−9), asthma (rg=0.50, [0.33-0.68], P=1.53 × 10−8) and IRD (rg=0.34, [0.09-0.58], P=6.97 × 10−3). Furthermore, we observed high genetic correlations between OSA comorbidities. Since many of OSA comorbidities are correlated with BMI, we calculated the genetic correlations after BMI adjustment. This analysis showed somewhat lower estimates for genetic correlations between OSA and CHD (rg=0.24 [0.012-0.47], P=0.04), depression (rg=0.33, [0.17-0.50], P=1.1 × 10−3), asthma (rg=0.33 [0.11-0.54], P=2.6 × 10−3) and hypothyroidism, (rg=0.28 [0.11-0.44], P=8.0 × 10−4). Genetic correlations between OSA and BMI (rg=0.08, [−0.05-0.22], P=0.22), hypertension (rg=0.05, [−0.10-0.20], P=0.51), T2D (rg=0.15, [−0.03-0.33], P=0.11), stroke (rg=0.32, [−0.05-0.69], P=0.09) and IRD (rg=0.27, [−0.01-0.54], P=5.7 × 10−2) attenuated after BMI adjustment (Figure 3).
To estimate genetic correlations between FinnGen OSA summary statistics and other sleep traits we used UKBB derived summary statistics for sleep variables. We observed genetic correlation with sleep efficiency13 rg = −0.31, [−0.44 - −0.17], P=9.80 × 10−6) and this was reflected with higher genetic correlation with daytime sleepiness29 (rg = 0.44, [0.33-0.54], P=1.27 × 10−15). These associations remained significant also after BMI adjustment (rg=-0.19, [−0.36 - −0.03], P= 0.02, rg=0.42, [0.29-0.55], P=1.06 × 10−10, respectively). We did not find significant genetic correlations between OSA and sleep duration or chronotype29 (Table 3).
To investigate the biological mechanisms behind OSA, we also examined tissue enrichment of association signals using partitioned heritability analysis using LDSC: an approach which combines data from ENCODE and the GTEx resources30, 31 to
FinnGen OSA summary statistics. Concordantly with the association of BMI and cardiometabolic traits, we observed strongest association with cardiovascular tissues and connective and bone tissues (P < 0.05). Furthermore, enrichments with BMI adjusted OSA implicated central nervous system (CNS) as the strongest associating single tissue (P < 0.05) (Supplementary Figure 3).
To test if there is a causal relationship between OSA and its comorbidities, we performed analysis of PRS followed by formal MR analysis using FinnGen OSA summary statistics and independent BMI SNPs32. The BMI PRS showed a strong association with OSA risk (Table 4) and the individuals in the highest BMI PRS quintile had 1.98-fold increased ([1.88-2.09], P=3.38 × 10−140) OSA risk after adjustment for age, sex and 10 first PCs. Similarly, this association was further accentuated in formal MR. We used 64 independent BMI SNPs32 as instrumental variables to predict OSA. In line with epidemiological observations and genetic correlation, we discovered a strong causal predictive effect from BMI to OSA (IVW: beta=0.67, P=8.32 × 10−16) (Figure 4, Supplementary Table 3).
Replication
For each lead variants associated with OSA, we examined the estimates from the additional, comparable cohorts: UKBB, ANDIS and EGCUT. The results were combined using inverse-variance weighted fixed-effect meta-analysis. These additional independent datasets support the role of FTO and GAPVD1 loci in OSA (P < 0.05) (Supplementary Table 4).
Discussion
In this study, using biobank data of over 217,000 individuals we show that OSA risk has a strong genetic component and identify five genetic loci that are associated with the risk for OSA. Our results show high genetic correlations between OSA and cardiometabolic diseases and risk factors, with strongest connections between OSA and BMI, hypertension, T2D and CHD, which are in line with previous epidemiological and clinical observations. These genetic correlations tracked with phenotypic correlations and comorbidities for OSA. In addition, both our association findings and the MR results support the causal role of obesity in OSA.
These results allow us to draw several conclusions. First, genetic variation plays an important role in development of OSA. This is supported by both the SNP heritability estimates and the associated loci.
Second, our results show that obesity plays a central causal role in the OSA risk. This is supported by high genetic correlations between OSA and BMI. We found that four out of five associated loci were mediated through their associations with BMI. These findings are in line with the finding that weight loss is an important contributor of lowering AHI and the severity of OSA39, 40.
Third, we also identified a strong association near RMST/NEDD1, which was specific for OSA independent of BMI. The lead SNP associated with antidepressant purchases which may imply that daytime sleepiness caused by OSA together with sleep disturbances may lead to depression and increased antidepressant usage. This is in line with the observation that depression is prevalent among patients with OSA8.
Fourth, a strong genetic correlation was observed between OSA and sleep traits, especially with sleepiness and sleep efficiency. These findings highlight the pathological effects of OSA on sleep. As OSA is manageable with Continuous Positive Airway Pressure (CPAP) or oral appliance, these genetic correlations implicate the importance of OSA treatment.
Our study does have some limitations. First, registry-based ascertainment through hospitalisation may miss non-hospitalised cases (false negatives) and treatment information such as CPAP compliance or oral sleep apnoea appliance usage. However, to our knowledge this is the largest number of cases combined for a
GWAS. Second, due to a relatively small number of cases in the replication datasets, our statistical power was limited in the replication analysis. The finding of rs185932673 should be interpreted cautiously as the variant is rare in the Finnish population and the association was not replicated in the other study samples.
Here we present associations between five novel genetic loci and OSA. Our findings highlight the causal links between obesity and OSA but also provide evidence for non-BMI dependent genetic effects. In addition to BMI, we show that genetic effects that modify risk of cardiometabolic diseases, depression, hypothyroidism, asthma and IRD are also correlated with genetic effects for OSA showing that the observed comorbidities between OSA and these diseases may have a joint genetic basis. Our results confirm that OSA is a heterogenic disease with several phenotypes and that implies different approach to OSA management.
Conflict of Interest
V.S. has received honoraria from Novo Nordisk and Sanofi for consultations and has ongoing research collaboration with Bayer AG (all unrelated to this study).
Author contributions
S.R. and T. P. supervised the study. S.E.R, S.S, H.M.O, M. K. and J.K. performed the statistical and bioinformatics analyses. A.S.H. and T.K. phenotyped study samples. S.S, H.M.O. and S.E.R. wrote the paper with the feedback from all co-authors.
Data availability
The FinnGen data may be accessed through Finnish Biobanks’ FinnBB portal (www.finbb.fi) and THL Biobank data may be accessed through THL Biobank (https://thl.fi/en/web/thl-biobank).
Code availability
The full genotyping and imputation protocol for FinnGen is described at https://doi-org.libproxy.helsinki.fi/10.17504/protocols.io.nmndc5e.
Contributors of FinnGen
Steering Committee
Aarno Palotie Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Mark Daly Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Pharmaceutical companies
Howard Jacob Abbvie, Chicago, IL, United States
Athena Matakidou Astra Zeneca, Cambridge, United Kingdom
Heiko Runz Biogen, Cambridge, MA, United States
Sally John Biogen, Cambridge, MA, United States
Robert Plenge Celgene, Summit, NJ, United States
Mark McCarthy Genentech, San Francisco, CA, United States
Julie Hunkapiller Genentech, San Francisco, CA, United States
Meg Ehm GlaxoSmithKline, Brentford, United Kingdom
Dawn Waterworth GlaxoSmithKline, Brentford, United Kingdom
Caroline Fox Merck, Kenilworth, NJ, United States
Anders Malarstig Pfizer, New York, NY, United States
Kathy Klinger Sanofi, Paris, France
Kathy Call Sanofi, Paris, France
University of Helsinki & Biobanks
Tomi Mäkelä HiLIFE, University of Helsinki, Finland, Finland
Jaakko Kaprio Institute for Molecular Medicine Finland, HiLIFE, Helsinki, Finland, Finland
Petri Virolainen Auria Biobank / Univ. of Turku / Hospital District of Southwest Finland, Turku, Finland
Kari Pulkki Auria Biobank / Univ. of Turku / Hospital District of Southwest Finland, Turku, Finland
Terhi Kilpi THL Biobank / Finnish Institute for Health and Welfare Helsinki, Finland
Markus Perola THL Biobank / Finnish Institute for Health and Welfare Helsinki, Finland
Jukka Partanen Finnish Red Cross Blood Service / Finnish Hematology Registry and Clinical Biobank, Helsinki, Finland
Anne Pitkäranta Hospital District of Helsinki and Uusimaa, Helsinki, Finland
Riitta Kaarteenaho Northern Finland Biobank Borealis / University of Oulu / Northern Ostrobothnia Hospital District, Oulu, Finland
Seppo Vainio Northern Finland Biobank Borealis / University of Oulu / Northern Ostrobothnia Hospital District, Oulu, Finland
Kimmo Savinainen Finnish Clinical Biobank Tampere / University of Tampere / Pirkanmaa Hospital District, Tampere, Finland
Veli-Matti Kosma Biobank of Eastern Finland / University of Eastern Finland / Northern Savo Hospital District, Kuopio, Finland
Urho Kujala Central Finland Biobank / University of Jyväskylä / Central Finland Health Care District, Jyväskylä, Finland
Other Experts/ Non-Voting Members
Outi Tuovila Business Finland, Helsinki, Finland
Minna Hendolin Business Finland, Helsinki, Finland
Raimo Pakkanen Business Finland, Helsinki, Finland
Scientific Committee
Pharmaceutical companies
Jeff Waring Abbvie, Chicago, IL, United States
Bridget Riley-Gillis Abbvie, Chicago, IL, United States
Athena Matakidou Astra Zeneca, Cambridge, United Kingdom
Heiko Runz Biogen, Cambridge, MA, United States
Jimmy Liu Biogen, Cambridge, MA, United States
Shameek Biswas Celgene, Summit, NJ, United States
Julie Hunkapiller Genentech, San Francisco, CA, United States
Dawn Waterworth GlaxoSmithKline, Brentford, United Kingdom
Meg Ehm GlaxoSmithKline, Brentford, United Kingdom
Dorothee Diogo Merck, Kenilworth, NJ, United States
Caroline Fox Merck, Kenilworth, NJ, United States
Anders Malarstig Pfizer, New York, NY, United States
Catherine Marshall Pfizer, New York, NY, United States
Xinli Hu Pfizer, New York, NY, United States
Kathy Call Sanofi, Paris, France
Kathy Klinger Sanofi, Paris, France
Matthias Gossel Sanofi, Paris, France
University of Helsinki & Biobanks
Samuli Ripatti Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
Johanna Schleutker Auria Biobank / Univ. of Turku / Hospital District of Southwest Finland, Turku, Finland
Markus Perola THL Biobank / Finnish Institute for Health and Welfare Helsinki, Finland
Mikko Arvas Finnish Red Cross Blood Service / Finnish Hematology Registry and Clinical Biobank, Helsinki, Finland
Olli Carpen Hospital District of Helsinki and Uusimaa, Helsinki, Finland
Reetta Hinttala Northern Finland Biobank Borealis / University of Oulu / Northern Ostrobothnia Hospital District, Oulu, Finland
Johannes Kettunen Northern Finland Biobank Borealis / University of Oulu / Northern Ostrobothnia Hospital District, Oulu, Finland
Reijo Laaksonen Finnish Clinical Biobank Tampere / University of Tampere / Pirkanmaa Hospital District, Tampere, Finland
Arto Mannermaa Biobank of Eastern Finland / University of Eastern Finland / Northern Savo Hospital District, Kuopio, Finland
Juha Paloneva Central Finland Biobank / University of Jyväskylä / Central Finland Health Care District, Jyväskylä, Finland
Urho Kujala Central Finland Biobank / University of Jyväskylä / Central Finland Health Care District, Jyväskylä, Finland
Other Experts/ Non-Voting Members
Outi Tuovila Business Finland, Helsinki, Finland
Minna Hendolin Business Finland, Helsinki, Finland
Raimo Pakkanen Business Finland, Helsinki, Finland
Clinical Groups
Neurology Group
Hilkka Soininen Northern Savo Hospital District, Kuopio, Finland
Valtteri Julkunen Northern Savo Hospital District, Kuopio, Finland
Anne Remes Northern Ostrobothnia Hospital District, Oulu, Finland
Reetta KälviäinenNorthern Savo Hospital District, Kuopio, Finland
Mikko Hiltunen Northern Savo Hospital District, Kuopio, Finland
Jukka Peltola Pirkanmaa Hospital District, Tampere, Finland
Pentti Tienari Hospital District of Helsinki and Uusimaa, Helsinki, Finland
Juha Rinne Hospital District of Southwest Finland, Turku, Finland
Adam Ziemann Abbvie, Chicago, IL, United States
Jeffrey Waring Abbvie, Chicago, IL, United States
Sahar Esmaeeli Abbvie, Chicago, IL, United States
Nizar Smaoui Abbvie, Chicago, IL, United States
Anne Lehtonen Abbvie, Chicago, IL, United States
Susan Eaton Biogen, Cambridge, MA, United States
Heiko Runz Biogen, Cambridge, MA, United States
Sanni Lahdenperä Biogen, Cambridge, MA, United States
Shameek Biswas Celgene, Summit, NJ, United States
John Michon Genentech, San Francisco, CA, United States
Geoff Kerchner Genentech, San Francisco, CA, United States
Julie Hunkapiller Genentech, San Francisco, CA, United States
Natalie Bowers Genentech, San Francisco, CA, United States
Edmond Teng Genentech, San Francisco, CA, United States
John Eicher Merck, Kenilworth, NJ, United States
Vinay Mehta Merck, Kenilworth, NJ, United States
Padhraig Gormley Merck, Kenilworth, NJ, United States
Kari Linden Pfizer, New York, NY, United States
Christopher Whelan Pfizer, New York, NY, United States
Fanli Xu GlaxoSmithKline, Brentford, United Kingdom
David Pulford GlaxoSmithKline, Brentford, United Kingdom
Gastroenterology Group
Martti Färkkilä Hospital District of Helsinki and Uusimaa, Helsinki, Finland
Sampsa Pikkarainen Hospital District of Helsinki and Uusimaa, Helsinki, Finland
Airi Jussila Pirkanmaa Hospital District, Tampere, Finland
Timo Blomster Northern Ostrobothnia Hospital District, Oulu, Finland
Mikko Kiviniemi Northern Savo Hospital District, Kuopio, Finland
Markku Voutilainen Hospital District of Southwest Finland, Turku, Finland Bob Georgantas Abbvie, Chicago, IL, United States
Graham Heap Abbvie, Chicago, IL, United States
Jeffrey Waring Abbvie, Chicago, IL, United States
Nizar Smaoui Abbvie, Chicago, IL, United States
Fedik Rahimov Abbvie, Chicago, IL, United States
Anne Lehtonen Abbvie, Chicago, IL, United States
Keith Usiskin Celgene, Summit, NJ, United States
Joseph Maranville Celgene, Summit, NJ, United States
Tim Lu Genentech, San Francisco, CA, United States
Natalie Bowers Genentech, San Francisco, CA, United States
Danny Oh Genentech, San Francisco, CA, United States
John Michon Genentech, San Francisco, CA, United States
Vinay Mehta Merck, Kenilworth, NJ, United States
Kirsi Kalpala Pfizer, New York, NY, United States
Melissa Miller Pfizer, New York, NY, United States
Xinli Hu Pfizer, New York, NY, United States
Linda McCarthy GlaxoSmithKline, Brentford, United Kingdom
Rheumatology Group
Kari Eklund Hospital District of Helsinki and Uusimaa, Helsinki, Finland
Antti Palomäki Hospital District of Southwest Finland, Turku, Finland
Pia Isomäki Pirkanmaa Hospital District, Tampere, Finland
Laura Pirilä Hospital District of Southwest Finland, Turku, Finland
Oili Kaipiainen-Seppänen Northern Savo Hospital District, Kuopio, Finland
Johanna Huhtakangas Northern Ostrobothnia Hospital District, Oulu, Finland
Bob Georgantas Abbvie, Chicago, IL, United States
Jeffrey Waring Abbvie, Chicago, IL, United States
Fedik Rahimov Abbvie, Chicago, IL, United States
Apinya Lertratanakul Abbvie, Chicago, IL, United States
Nizar Smaoui Abbvie, Chicago, IL, United States
Anne Lehtonen Abbvie, Chicago, IL, United States
David Close Astra Zeneca, Cambridge, United Kingdom
Marla Hochfeld Celgene, Summit, NJ, United States
Natalie Bowers Genentech, San Francisco, CA, United States
John Michon Genentech, San Francisco, CA, United States
Dorothee Diogo Merck, Kenilworth, NJ, United States
Vinay Mehta Merck, Kenilworth, NJ, United States
Kirsi Kalpala Pfizer, New York, NY, United States
Nan Bing Pfizer, New York, NY, United States
Xinli Hu Pfizer, New York, NY, United States
Jorge Esparza Gordillo GlaxoSmithKline, Brentford, United Kingdom
Nina Mars Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
Pulmonology Group
Tarja Laitinen Pirkanmaa Hospital District, Tampere, Finland
Margit Pelkonen Northern Savo Hospital District, Kuopio, Finland
Paula Kauppi Hospital District of Helsinki and Uusimaa, Helsinki, Finland
Hannu Kankaanranta Pirkanmaa Hospital District, Tampere, Finland
Terttu Harju Northern Ostrobothnia Hospital District, Oulu, Finland
Nizar Smaoui Abbvie, Chicago, IL, United States
David Close Astra Zeneca, Cambridge, United Kingdom
Steven GreenbergCelgene, Summit, NJ, United States
Hubert Chen Genentech, San Francisco, CA, United States
Natalie Bowers Genentech, San Francisco, CA, United States
John Michon Genentech, San Francisco, CA, United States
Vinay Mehta Merck, Kenilworth, NJ, United States
Jo Betts GlaxoSmithKline, Brentford, United Kingdom
Soumitra Ghosh GlaxoSmithKline, Brentford, United Kingdom
Cardiometabolic Diseases Group
Veikko Salomaa Finnish Institute for Health and Welfare Helsinki, Finland
Teemu Niiranen Finnish Institute for Health and Welfare Helsinki, Finland
Markus Juonala Hospital District of Southwest Finland, Turku, Finland
Kaj Metsärinne Hospital District of Southwest Finland, Turku, Finland
Mika Kähönen Pirkanmaa Hospital District, Tampere, Finland
Juhani Junttila Northern Ostrobothnia Hospital District, Oulu, Finland
Markku Laakso Northern Savo Hospital District, Kuopio, Finland
Jussi Pihlajamäki Northern Savo Hospital District, Kuopio, Finland
Juha Sinisalo Hospital District of Helsinki and Uusimaa, Helsinki, Finland
Marja-Riitta Taskinen Hospital District of Helsinki and Uusimaa, Helsinki, Finland
Tiinamaija Tuomi Hospital District of Helsinki and Uusimaa, Helsinki, Finland
Jari Laukkanen Central Finland Health Care District, Jyväskylä, Finland
Ben Challis Astra Zeneca, Cambridge, United Kingdom
Andrew Peterson Genentech, San Francisco, CA, United States Julie Hunkapiller Genentech, San Francisco, CA, United States
Natalie Bowers Genentech, San Francisco, CA, United States
John Michon Genentech, San Francisco, CA, United States
Dorothee Diogo Merck, Kenilworth, NJ, United States
Audrey Chu Merck, Kenilworth, NJ, United States
Vinay Mehta Merck, Kenilworth, NJ, United States
Jaakko Parkkinen Pfizer, New York, NY, United States
Melissa Miller Pfizer, New York, NY, United States
Anthony Muslin Sanofi, Paris, France
Dawn Waterworth GlaxoSmithKline, Brentford, United Kingdom
Oncology Group
Heikki Joensuu Hospital District of Helsinki and Uusimaa, Helsinki, Finland
Tuomo Meretoja Hospital District of Helsinki and Uusimaa, Helsinki, Finland
Olli Carpen Hospital District of Helsinki and Uusimaa, Helsinki, Finland
Lauri Aaltonen Hospital District of Helsinki and Uusimaa, Helsinki, Finland
Annika Auranen Pirkanmaa Hospital District, Tampere, Finland
Peeter Karihtala Northern Ostrobothnia Hospital District, Oulu, Finland
Saila Kauppila Northern Ostrobothnia Hospital District, Oulu, Finland
Päivi Auvinen Northern Savo Hospital District, Kuopio, Finland
Klaus Elenius Hospital District of Southwest Finland, Turku, Finland
Relja Popovic Abbvie, Chicago, IL, United States
Jeffrey Waring Abbvie, Chicago, IL, United States
Bridget Riley-Gillis Abbvie, Chicago, IL, United States
Anne Lehtonen Abbvie, Chicago, IL, United States
Athena Matakidou Astra Zeneca, Cambridge, United Kingdom
Jennifer Schutzman Genentech, San Francisco, CA, United States
Julie Hunkapiller Genentech, San Francisco, CA, United States
Natalie Bowers Genentech, San Francisco, CA, United States
John Michon Genentech, San Francisco, CA, United States
Vinay Mehta Merck, Kenilworth, NJ, United States
Andrey Loboda Merck, Kenilworth, NJ, United States
Aparna Chhibber Merck, Kenilworth, NJ, United States
Heli Lehtonen Pfizer, New York, NY, United States
Stefan McDonough Pfizer, New York, NY, United States
Marika Crohns Sanofi, Paris, France
Diptee Kulkarni GlaxoSmithKline, Brentford, United Kingdom
Opthalmology Group
Kai Kaarniranta Northern Savo Hospital District, Kuopio, Finland
Joni Turunen Hospital District of Helsinki and Uusimaa, Helsinki, Finland
Terhi Ollila Hospital District of Helsinki and Uusimaa, Helsinki, Finland
Sanna Seitsonen Hospital District of Helsinki and Uusimaa, Helsinki, Finland
Hannu Uusitalo Pirkanmaa Hospital District, Tampere, Finland
Vesa Aaltonen Hospital District of Southwest Finland, Turku, Finland
Hannele Uusitalo-Järvinen Pirkanmaa Hospital District, Tampere, Finland
Marja LuodonpääNorthern Ostrobothnia Hospital District, Oulu, Finland
Nina Hautala Northern Ostrobothnia Hospital District, Oulu, Finland
Heiko Runz Biogen, Cambridge, MA, United States
Erich Strauss Genentech, San Francisco, CA, United States
Natalie Bowers Genentech, San Francisco, CA, United States
Hao Chen Genentech, San Francisco, CA, United States
John Michon Genentech, San Francisco, CA, United States
Anna Podgornaia Merck, Kenilworth, NJ, United States
Vinay Mehta Merck, Kenilworth, NJ, United States
Dorothee Diogo Merck, Kenilworth, NJ, United States
Joshua Hoffman GlaxoSmithKline, Brentford, United Kingdom
Dermatology Group
Kaisa Tasanen Northern Ostrobothnia Hospital District, Oulu, Finland
Laura Huilaja Northern Ostrobothnia Hospital District, Oulu, Finland
Katariina Hannula-Jouppi Hospital District of Helsinki and Uusimaa, Helsinki, Finland
Teea Salmi Pirkanmaa Hospital District, Tampere, Finland
Sirkku Peltonen Hospital District of Southwest Finland, Turku, Finland
Leena Koulu Hospital District of Southwest Finland, Turku, Finland
Ilkka Harvima Northern Savo Hospital District, Kuopio, Finland
Kirsi Kalpala Pfizer, New York, NY, United States
Ying Wu Pfizer, New York, NY, United States
David Choy Genentech, San Francisco, CA, United States
John Michon Genentech, San Francisco, CA, United States
Nizar Smaoui Abbvie, Chicago, IL, United States
Fedik Rahimov Abbvie, Chicago, IL, United States
Anne Lehtonen Abbvie, Chicago, IL, United States
Dawn Waterworth GlaxoSmithKline, Brentford, United Kingdom
FinnGen Teams
Administration Team
Anu Jalanko Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Risto Kajanne Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Ulrike Lyhs Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Communication
Mari Kaunisto Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Analysis Team
Justin Wade Davis Abbvie, Chicago, IL, United States
Bridget Riley-Gillis Abbvie, Chicago, IL, United States
Danjuma Quarless Abbvie, Chicago, IL, United States
Slavé Petrovski Astra Zeneca, Cambridge, United Kingdom
Jimmy Liu Biogen, Cambridge, MA, United States
Chia-Yen Chen Biogen, Cambridge, MA, United States
Paola Bronson Biogen, Cambridge, MA, United States
Robert Yang Celgene, Summit, NJ, United States
Joseph Maranville Celgene, Summit, NJ, United States
Shameek Biswas Celgene, Summit, NJ, United States
Diana Chang Genentech, San Francisco, CA, United States
Julie Hunkapiller Genentech, San Francisco, CA, United States
Tushar Bhangale Genentech, San Francisco, CA, United States
Natalie Bowers Genentech, San Francisco, CA, United States
Dorothee Diogo Merck, Kenilworth, NJ, United States
Emily Holzinger Merck, Kenilworth, NJ, United States
Padhraig Gormley Merck, Kenilworth, NJ, United States
Xulong Wang Merck, Kenilworth, NJ, United States
Xing Chen Pfizer, New York, NY, United States
Åsa Hedman Pfizer, New York, NY, United States
Kirsi Auro GlaxoSmithKline, Brentford, United Kingdom
Clarence Wang Sanofi, Paris, France
Ethan Xu Sanofi, Paris, France
Franck Auge Sanofi, Paris, France
Clement Chatelain Sanofi, Paris, France
Mitja Kurki Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland /
Broad Institute, Cambridge, MA, United States
Samuli Ripatti Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Mark Daly Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Juha Karjalainen Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland /
Broad Institute, Cambridge, MA, United States
Aki Havulinna Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Anu Jalanko Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Kimmo Palin University of Helsinki, Helsinki, Finland
Priit Palta Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Pietro Della Briotta Parolo Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland Wei Zhou Broad Institute, Cambridge, MA, United States
Susanna Lemmelä Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Manuel Rivas University of Stanford, Stanford, CA, United States
Jarmo Harju Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Aarno Palotie Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Arto Lehisto Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Andrea Ganna Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Vincent Llorens Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Antti Karlsson Auria Biobank / Univ. of Turku / Hospital District of Southwest Finland, Turku, Finland
Kati Kristiansson THL Biobank / Finnish Institute for Health and Welfare Helsinki, Finland
Mikko Arvas Finnish Red Cross Blood Service / Finnish Hematology Registry and Clinical Biobank, Helsinki, Finland
Kati Hyvärinen Finnish Red Cross Blood Service / Finnish Hematology Registry and Clinical Biobank, Helsinki, Finland
Jarmo Ritari Finnish Red Cross Blood Service / Finnish Hematology Registry and Clinical Biobank, Helsinki, Finland
Tiina Wahlfors Finnish Red Cross Blood Service / Finnish Hematology Registry and Clinical Biobank, Helsinki, Finland
Miika Koskinen Hospital District of Helsinki and Uusimaa, Helsinki, Finland BB/HUS/Univ Hosp Districts
Olli Carpen Hospital District of Helsinki and Uusimaa, Helsinki, Finland BB/HUS/Univ Hosp Districts
Johannes Kettunen Northern Finland Biobank Borealis / University of Oulu / Northern Ostrobothnia Hospital District, Oulu, Finland
Katri Pylkäs Northern Finland Biobank Borealis / University of Oulu / Northern Ostrobothnia Hospital District, Oulu, Finland
Marita Kalaoja Northern Finland Biobank Borealis / University of Oulu / Northern Ostrobothnia
Hospital District, Oulu, Finland
Minna Karjalainen Northern Finland Biobank Borealis / University of Oulu / Northern Ostrobothnia Hospital District, Oulu, Finland
Tuomo Mantere Northern Finland Biobank Borealis / University of Oulu / Northern Ostrobothnia
Hospital District, Oulu, Finland
Eeva Kangasniemi Finnish Clinical Biobank Tampere / University of Tampere / Pirkanmaa Hospital District, Tampere, Finland
Sami Heikkinen Biobank of Eastern Finland / University of Eastern Finland / Northern Savo Hospital District, Kuopio, Finland
Arto Mannermaa Biobank of Eastern Finland / University of Eastern Finland / Northern Savo Hospital District, Kuopio, Finland
Eija Laakkonen Central Finland Biobank / University of Jyväskylä / Central Finland Health Care District, Jyväskylä, Finland
Juha Kononen Central Finland Biobank / University of Jyväskylä / Central Finland Health Care District, Jyväskylä, Finland
Sample Collection Coordination
Anu Loukola Hospital District of Helsinki and Uusimaa, Helsinki, Finland
Sample Logistics
Päivi Laiho THL Biobank / Finnish Institute for Health and Welfare Helsinki, Finland
Tuuli Sistonen THL Biobank / Finnish Institute for Health and Welfare Helsinki, Finland
Essi Kaiharju THL Biobank / Finnish Institute for Health and Welfare Helsinki, Finland
Markku Laukkanen THL Biobank / Finnish Institute for Health and Welfare Helsinki, Finland
Elina Järvensivu THL Biobank / Finnish Institute for Health and Welfare Helsinki, Finland
Sini Lähteenmäki THL Biobank / Finnish Institute for Health and Welfare Helsinki, Finland
Lotta Männikkö THL Biobank / Finnish Institute for Health and Welfare Helsinki, Finland
Regis Wong THL Biobank / Finnish Institute for Health and Welfare Helsinki, Finland
Registry Data Operations
Kati Kristiansson THL Biobank / Finnish Institute for Health and Welfare Helsinki, Finland
Hannele Mattsson THL Biobank / Finnish Institute for Health and Welfare Helsinki, Finland
Susanna Lemmelä Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Tero Hiekkalinna THL Biobank / Finnish Institute for Health and Welfare Helsinki, Finland
Manuel González Jiménez. THL Biobank / Finnish Institute for Health and Welfare Helsinki, Finland
Genotyping
Kati Donner Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Sequencing Informatics
Priit Palta Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Kalle Pärn Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Javier Nunez-Fontarnau Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Data Management and IT Infrastructure
Jarmo Harju Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Elina Kilpeläinen Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Timo P. Sipilä Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Georg Brein Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Alexander Dada Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Ghazal Awaisa Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Anastasia Shcherban Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Tuomas Sipilä Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Clinical Endpoint Development
Hannele Laivuori Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Aki Havulinna Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Susanna Lemmelä Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Tuomo Kiiskinen Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Finland
Trajectory Team
Tarja Laitinen Pirkanmaa Hospital District, Tampere, Finland
Harri Siirtola University of Tampere, Tampere, Finland
Javier Gracia Tabuenca University of Tampere, Tampere, Finland
Biobank Directors
Lila Kallio Auria Biobank, Turku, Finland
Sirpa Soini THL Biobank, Helsinki, Finland
Jukka Partanen Blood Service Biobank, Helsinki, Finland
Kimmo Pitkänen Helsinki Biobank, Helsinki, Finland
Seppo Vainio Northern Finland Biobank Borealis, Oulu, Finland
Kimmo Savinainen Tampere Biobank, Tampere, Finland
Veli-Matti Kosma Biobank of Eastern Finland, Kuopio, Finland
Teijo Kuopio Central Finland Biobank, Jyväskylä, Finlan
Acknowledgements
We would like to thank all participants of the FinnGen study for their generous participation. We would also like to thank Sari Kivikko for management assistance. Patients and controls in FinnGen provided informed consent for biobank research, based on the Finnish Biobank Act. Alternatively, older research cohorts, collected prior to the start of FinnGen (in August 2017), were collected based on study-specific consents and later transferred to the Finnish biobanks after approval by Valvira, the National Supervisory Authority for Welfare and Health. Recruitment protocols followed the biobank protocols approved by Valvira. The Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa (HUS) approved the FinnGen study protocol Nr HUS/990/2017.
The FinnGen study is approved by the Finnish Institute for Health and Welfare (THL), approval numberTHL/2031/6.02.00/2017,amendmentsTHL/1101/5.05.00/2017,THL/341/6.02.00/2018,THL/2222/6.02.00/2018,THL/283/6.02.00/2019,THL/1721/5.05.00/2019,
Digital and population data service agency VRK43431/2017-3, VRK/6909/2018-3, VRK/4415/2019-3 the Social Insurance Institution (KELA) KELA 58/522/2017, KELA 131/522/2018, KELA 70/522/2019, KELA 98/522/2019, and Statistics Finland TK-53-1041-17.
The Biobank Access Decisions for FinnGen samples and data utilised in FinnGen Data Freeze 5 include: THL Biobank BB2017_55, BB2017_111, BB2018_19, BB_2018_34, BB_2018_67, BB2018_71, BB2019_7, BB2019_8, BB2019_26, Finnish Red Cross Blood Service Biobank 7.12.2017, Helsinki Biobank HUS/359/2017, Auria Biobank AB17-5154, Biobank Borealis of Northern Finland_2017_1013, Biobank of Eastern Finland 1186/2018, Finnish Clinical Biobank Tampere MH0004, Central Finland Biobank 1-2017, and Terveystalo Biobank STB 2018001.
This research has been conducted using the UK Biobank Resource under Application Number 22627.
This work was supported by the Academy of Finland Center of Excellence in Complex Disease Genetics [Grant No 312062 to S.R., 312074 to A.P.]; Academy of Finland [Grant No 285380 to S.R, 128650 to A.P, 309643 to H.M.O]; the Finnish Foundation for Cardiovascular Research [to S.R., V.S., and A.P.]; the Sigrid Jusélius Foundation [to S.R. and A.P.]; University of Helsinki HiLIFE Fellow grants 2017-2020 [to S.R.] and Foundation and the Horizon 2020 Research and Innovation Programme [grant number 667301 (COSYN) to A.P.]; Oskar Öfflund foundation and Yrjö Jahnsson foundation [to H.M.O]; The Finnish Dental Society Apollonia [to S.S].
The FinnGen project is funded by two grants from Business Finland (HUS 4685/31/2016 and UH 4386/31/2016) and eleven industry partners (AbbVie Inc, AstraZeneca UK Ltd, Biogen MA Inc, Celgene Corporation, Celgene International II Sàrl, Genentech Inc, Merck Sharp & Dohme Corp, Pfizer Inc., GlaxoSmithKline, Sanofi, Maze Therapeutics Inc., Janssen Biotech Inc). Following biobanks are acknowledged for collecting the FinnGen project samples: Auria Biobank (https://www.auria.fi/biopankki), THL Biobank (https://thl.fi/fi/web/thl-biopankki), Helsinki Biobank (https://www.terveyskyla.fi/helsinginbiopankki), Biobank Borealis of Northern Finland (https://www.oulu.fi/university/node/38474), Finnish Clinical Biobank Tampere (https://www.tays.fi/en-US/Research_and_development/Finnish_Clinical_Biobank_Tampere), Biobank of Eastern Finland (https://ita-suomenbiopankki.fi), Central Finland Biobank (https://www.ksshp.fi/fi-FI/Potilaalle/Biopankki), Finnish Red Cross Blood Service Biobank (https://www.veripalvelu.fi/verenluovutus/biopankkitoiminta) and Terveystalo Biobank (https://www.terveystalo.com/fi/Yritystietoa/Terveystalo-Biopankki/Biopankki/). All Finnish Biobanks are members of BBMRI.fi infrastructure (www.bbmri.fi).
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.